Design of passivity and passification for delayed neural networks with Markovian jump parameters via non-uniform sampled-data control

The motivation behind this paper is to explore the issue of passivity and passification for delayed neural networks with Markov jump parameters. A state feedback control approach with non-uniform sampling period is considered. The interest of this paper lies in the thought about another basic uniqueness wound up being less conservatism than watched Jensen's inequality and takes totally the relationship between the terms in the Leibniz-Newton formula inside the arrangement of linear matrix inequalities. By utilizing the Lyapunov-Krasovskii functional strategy, a novel delay-dependent passivity criterion is developed with respect to linear matrix inequalities to guarantee the Markov jump delayed neural frameworks to be passive. Passivity and passification problems are tackled by using mode-dependent non-uniform sampled-data control. Using many examples from the literature, it is exhibited that the proposed stabilization theorem is less direct than past results. Finally, the framework is associated with benchmark issue, exhibiting to gather compelling stability criteria for reasonable issues, using the proposed strategy.